Date of Original Version
Abstract or Description
We describe a brieﬁng system that learns to predict the contents of reports generated by users who create periodic (weekly) reports as part of their normal activity. We address the question whether data derived from the implicit supervision provided by end-users is robust enough to support not only model parameter tuning but also a form of feature discovery. The system was evaluated under realistic conditions, by collecting data in a project-based university course where student group leaders were tasked with preparing weekly reports for the beneﬁt of the instructors, using the material from individual student reports.